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Add cuda::Stream capability to cuda::HOG::compute
In the previous version only the default stream was/could be used, i.e. cv::cuda::Stream::Null(). With this change, HOG::compute() will now run in parallel over different cuda::Streams. The code has been reordered so that all data allocation is completed first, then all the kernels are run in parallel over streams. Fix #8177
This commit is contained in:
Claudio
parent
f109c0134f
commit
35f66340d7
@ -52,6 +52,7 @@ namespace cv { namespace cuda { namespace device
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namespace hog
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{
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__constant__ int cnbins;
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__constant__ int cblock_stride_x;
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__constant__ int cblock_stride_y;
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@ -99,27 +100,28 @@ namespace cv { namespace cuda { namespace device
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void set_up_constants(int nbins, int block_stride_x, int block_stride_y,
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int nblocks_win_x, int nblocks_win_y, int ncells_block_x, int ncells_block_y)
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int nblocks_win_x, int nblocks_win_y, int ncells_block_x, int ncells_block_y,
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const cudaStream_t& stream)
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{
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cudaSafeCall( cudaMemcpyToSymbol(cnbins, &nbins, sizeof(nbins)) );
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cudaSafeCall( cudaMemcpyToSymbol(cblock_stride_x, &block_stride_x, sizeof(block_stride_x)) );
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cudaSafeCall( cudaMemcpyToSymbol(cblock_stride_y, &block_stride_y, sizeof(block_stride_y)) );
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cudaSafeCall( cudaMemcpyToSymbol(cnblocks_win_x, &nblocks_win_x, sizeof(nblocks_win_x)) );
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cudaSafeCall( cudaMemcpyToSymbol(cnblocks_win_y, &nblocks_win_y, sizeof(nblocks_win_y)) );
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cudaSafeCall( cudaMemcpyToSymbol(cncells_block_x, &ncells_block_x, sizeof(ncells_block_x)) );
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cudaSafeCall( cudaMemcpyToSymbol(cncells_block_y, &ncells_block_y, sizeof(ncells_block_y)) );
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cudaSafeCall(cudaMemcpyToSymbolAsync(cnbins, &nbins, sizeof(nbins), 0, cudaMemcpyHostToDevice, stream));
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cudaSafeCall(cudaMemcpyToSymbolAsync(cblock_stride_x, &block_stride_x, sizeof(block_stride_x), 0, cudaMemcpyHostToDevice, stream));
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cudaSafeCall(cudaMemcpyToSymbolAsync(cblock_stride_y, &block_stride_y, sizeof(block_stride_y), 0, cudaMemcpyHostToDevice, stream));
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cudaSafeCall(cudaMemcpyToSymbolAsync(cnblocks_win_x, &nblocks_win_x, sizeof(nblocks_win_x), 0, cudaMemcpyHostToDevice, stream));
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cudaSafeCall(cudaMemcpyToSymbolAsync(cnblocks_win_y, &nblocks_win_y, sizeof(nblocks_win_y), 0, cudaMemcpyHostToDevice, stream));
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cudaSafeCall(cudaMemcpyToSymbolAsync(cncells_block_x, &ncells_block_x, sizeof(ncells_block_x), 0, cudaMemcpyHostToDevice, stream));
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cudaSafeCall(cudaMemcpyToSymbolAsync(cncells_block_y, &ncells_block_y, sizeof(ncells_block_y), 0, cudaMemcpyHostToDevice, stream));
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int block_hist_size = nbins * ncells_block_x * ncells_block_y;
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cudaSafeCall( cudaMemcpyToSymbol(cblock_hist_size, &block_hist_size, sizeof(block_hist_size)) );
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cudaSafeCall(cudaMemcpyToSymbolAsync(cblock_hist_size, &block_hist_size, sizeof(block_hist_size), 0, cudaMemcpyHostToDevice, stream));
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int block_hist_size_2up = power_2up(block_hist_size);
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cudaSafeCall( cudaMemcpyToSymbol(cblock_hist_size_2up, &block_hist_size_2up, sizeof(block_hist_size_2up)) );
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cudaSafeCall(cudaMemcpyToSymbolAsync(cblock_hist_size_2up, &block_hist_size_2up, sizeof(block_hist_size_2up), 0, cudaMemcpyHostToDevice, stream));
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int descr_width = nblocks_win_x * block_hist_size;
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cudaSafeCall( cudaMemcpyToSymbol(cdescr_width, &descr_width, sizeof(descr_width)) );
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cudaSafeCall(cudaMemcpyToSymbolAsync(cdescr_width, &descr_width, sizeof(descr_width), 0, cudaMemcpyHostToDevice, stream));
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int descr_size = descr_width * nblocks_win_y;
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cudaSafeCall( cudaMemcpyToSymbol(cdescr_size, &descr_size, sizeof(descr_size)) );
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cudaSafeCall(cudaMemcpyToSymbolAsync(cdescr_size, &descr_size, sizeof(descr_size), 0, cudaMemcpyHostToDevice, stream));
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}
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@ -233,7 +235,8 @@ namespace cv { namespace cuda { namespace device
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void compute_hists(int nbins, int block_stride_x, int block_stride_y,
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int height, int width, const PtrStepSzf& grad,
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const PtrStepSzb& qangle, float sigma, float* block_hists,
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int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y)
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int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y,
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const cudaStream_t& stream)
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{
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const int ncells_block = ncells_block_x * ncells_block_y;
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const int patch_side = cell_size_x / 4;
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@ -259,20 +262,15 @@ namespace cv { namespace cuda { namespace device
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int final_hists_size = (nbins * ncells_block * nblocks) * sizeof(float);
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int smem = hists_size + final_hists_size;
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if (nblocks == 4)
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compute_hists_kernel_many_blocks<4><<<grid, threads, smem>>>(
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img_block_width, grad, qangle, scale, block_hists, cell_size_x, patch_size, block_patch_size, threads_cell, threads_block, half_cell_size);
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compute_hists_kernel_many_blocks<4><<<grid, threads, smem, stream>>>(img_block_width, grad, qangle, scale, block_hists, cell_size_x, patch_size, block_patch_size, threads_cell, threads_block, half_cell_size);
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else if (nblocks == 3)
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compute_hists_kernel_many_blocks<3><<<grid, threads, smem>>>(
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img_block_width, grad, qangle, scale, block_hists, cell_size_x, patch_size, block_patch_size, threads_cell, threads_block, half_cell_size);
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compute_hists_kernel_many_blocks<3><<<grid, threads, smem, stream>>>(img_block_width, grad, qangle, scale, block_hists, cell_size_x, patch_size, block_patch_size, threads_cell, threads_block, half_cell_size);
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else if (nblocks == 2)
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compute_hists_kernel_many_blocks<2><<<grid, threads, smem>>>(
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img_block_width, grad, qangle, scale, block_hists, cell_size_x, patch_size, block_patch_size, threads_cell, threads_block, half_cell_size);
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compute_hists_kernel_many_blocks<2><<<grid, threads, smem, stream>>>(img_block_width, grad, qangle, scale, block_hists, cell_size_x, patch_size, block_patch_size, threads_cell, threads_block, half_cell_size);
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else
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compute_hists_kernel_many_blocks<1><<<grid, threads, smem>>>(
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img_block_width, grad, qangle, scale, block_hists, cell_size_x, patch_size, block_patch_size, threads_cell, threads_block, half_cell_size);
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cudaSafeCall( cudaGetLastError() );
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compute_hists_kernel_many_blocks<1><<<grid, threads, smem, stream>>>(img_block_width, grad, qangle, scale, block_hists, cell_size_x, patch_size, block_patch_size, threads_cell, threads_block, half_cell_size);
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cudaSafeCall( cudaDeviceSynchronize() );
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cudaSafeCall( cudaGetLastError() );
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}
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@ -348,7 +346,8 @@ namespace cv { namespace cuda { namespace device
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void normalize_hists(int nbins, int block_stride_x, int block_stride_y,
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int height, int width, float* block_hists, float threshold, int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y)
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int height, int width, float* block_hists, float threshold, int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y,
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const cudaStream_t& stream)
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{
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const int nblocks = 1;
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@ -361,21 +360,19 @@ namespace cv { namespace cuda { namespace device
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dim3 grid(divUp(img_block_width, nblocks), img_block_height);
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if (nthreads == 32)
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normalize_hists_kernel_many_blocks<32, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
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normalize_hists_kernel_many_blocks<32, nblocks><<<grid, threads, 0, stream>>>(block_hist_size, img_block_width, block_hists, threshold);
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else if (nthreads == 64)
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normalize_hists_kernel_many_blocks<64, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
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normalize_hists_kernel_many_blocks<64, nblocks><<<grid, threads, 0, stream>>>(block_hist_size, img_block_width, block_hists, threshold);
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else if (nthreads == 128)
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normalize_hists_kernel_many_blocks<128, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
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normalize_hists_kernel_many_blocks<128, nblocks><<<grid, threads, 0, stream>>>(block_hist_size, img_block_width, block_hists, threshold);
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else if (nthreads == 256)
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normalize_hists_kernel_many_blocks<256, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
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normalize_hists_kernel_many_blocks<256, nblocks><<<grid, threads, 0, stream>>>(block_hist_size, img_block_width, block_hists, threshold);
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else if (nthreads == 512)
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normalize_hists_kernel_many_blocks<512, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold);
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normalize_hists_kernel_many_blocks<512, nblocks><<<grid, threads, 0, stream>>>(block_hist_size, img_block_width, block_hists, threshold);
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else
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CV_Error(cv::Error::StsBadArg, "normalize_hists: histogram's size is too big, try to decrease number of bins");
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cudaSafeCall( cudaGetLastError() );
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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@ -581,7 +578,8 @@ namespace cv { namespace cuda { namespace device
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void extract_descrs_by_cols(int win_height, int win_width, int block_stride_y, int block_stride_x,
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int win_stride_y, int win_stride_x, int height, int width, float* block_hists, int cell_size_x, int ncells_block_x,
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PtrStepSzf descriptors)
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PtrStepSzf descriptors,
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const cudaStream_t& stream)
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{
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const int nthreads = 256;
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@ -593,11 +591,9 @@ namespace cv { namespace cuda { namespace device
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dim3 grid(img_win_width, img_win_height);
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int img_block_width = (width - ncells_block_x * cell_size_x + block_stride_x) / block_stride_x;
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extract_descrs_by_cols_kernel<nthreads><<<grid, threads>>>(
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img_block_width, win_block_stride_x, win_block_stride_y, block_hists, descriptors);
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cudaSafeCall( cudaGetLastError() );
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extract_descrs_by_cols_kernel<nthreads><<<grid, threads, 0, stream>>>(img_block_width, win_block_stride_x, win_block_stride_y, block_hists, descriptors);
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cudaSafeCall( cudaDeviceSynchronize() );
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cudaSafeCall( cudaGetLastError() );
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}
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//----------------------------------------------------------------------------
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@ -708,7 +704,8 @@ namespace cv { namespace cuda { namespace device
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void compute_gradients_8UC4(int nbins, int height, int width, const PtrStepSzb& img,
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float angle_scale, PtrStepSzf grad, PtrStepSzb qangle, bool correct_gamma)
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float angle_scale, PtrStepSzf grad, PtrStepSzb qangle, bool correct_gamma,
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const cudaStream_t& stream)
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{
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(void)nbins;
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const int nthreads = 256;
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@ -717,13 +714,11 @@ namespace cv { namespace cuda { namespace device
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dim3 gdim(divUp(width, bdim.x), divUp(height, bdim.y));
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if (correct_gamma)
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compute_gradients_8UC4_kernel<nthreads, 1><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle);
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compute_gradients_8UC4_kernel<nthreads, 1><<<gdim, bdim, 0, stream>>>(height, width, img, angle_scale, grad, qangle);
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else
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compute_gradients_8UC4_kernel<nthreads, 0><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle);
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compute_gradients_8UC4_kernel<nthreads, 0><<<gdim, bdim, 0, stream>>>(height, width, img, angle_scale, grad, qangle);
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cudaSafeCall( cudaGetLastError() );
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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template <int nthreads, int correct_gamma>
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@ -66,15 +66,18 @@ namespace cv { namespace cuda { namespace device
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{
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void set_up_constants(int nbins, int block_stride_x, int block_stride_y,
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int nblocks_win_x, int nblocks_win_y,
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int ncells_block_x, int ncells_block_y);
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int ncells_block_x, int ncells_block_y,
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const cudaStream_t& stream);
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void compute_hists(int nbins, int block_stride_x, int block_stride_y,
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int height, int width, const PtrStepSzf& grad,
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const PtrStepSzb& qangle, float sigma, float* block_hists,
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int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y);
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int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y,
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const cudaStream_t& stream);
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void normalize_hists(int nbins, int block_stride_x, int block_stride_y,
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int height, int width, float* block_hists, float threshold, int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y);
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int height, int width, float* block_hists, float threshold, int cell_size_x, int cell_size_y, int ncells_block_x, int ncells_block_y,
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const cudaStream_t& stream);
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void classify_hists(int win_height, int win_width, int block_stride_y,
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int block_stride_x, int win_stride_y, int win_stride_x, int height,
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@ -90,12 +93,14 @@ namespace cv { namespace cuda { namespace device
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cv::cuda::PtrStepSzf descriptors);
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void extract_descrs_by_cols(int win_height, int win_width, int block_stride_y, int block_stride_x,
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int win_stride_y, int win_stride_x, int height, int width, float* block_hists, int cell_size_x, int ncells_block_x,
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cv::cuda::PtrStepSzf descriptors);
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cv::cuda::PtrStepSzf descriptors,
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const cudaStream_t& stream);
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void compute_gradients_8UC1(int nbins, int height, int width, const cv::cuda::PtrStepSzb& img,
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float angle_scale, cv::cuda::PtrStepSzf grad, cv::cuda::PtrStepSzb qangle, bool correct_gamma);
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void compute_gradients_8UC4(int nbins, int height, int width, const cv::cuda::PtrStepSzb& img,
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float angle_scale, cv::cuda::PtrStepSzf grad, cv::cuda::PtrStepSzb qangle, bool correct_gamma);
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float angle_scale, cv::cuda::PtrStepSzf grad, cv::cuda::PtrStepSzb qangle, bool correct_gamma,
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const cudaStream_t& stream);
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void resize_8UC1(const cv::cuda::PtrStepSzb& src, cv::cuda::PtrStepSzb dst);
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void resize_8UC4(const cv::cuda::PtrStepSzb& src, cv::cuda::PtrStepSzb dst);
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@ -182,8 +187,8 @@ namespace
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private:
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int getTotalHistSize(Size img_size) const;
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void computeBlockHistograms(const GpuMat& img, GpuMat& block_hists);
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void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);
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void computeBlockHistograms(const GpuMat& img, GpuMat& block_hists, Stream& stream);
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// void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle, Stream& stream);
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// Coefficients of the separating plane
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float free_coef_;
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@ -310,7 +315,7 @@ namespace
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BufferPool pool(Stream::Null());
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GpuMat block_hists = pool.getBuffer(1, getTotalHistSize(img.size()), CV_32FC1);
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computeBlockHistograms(img, block_hists);
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computeBlockHistograms(img, block_hists, Stream::Null());
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Size wins_per_img = numPartsWithin(img.size(), win_size_, win_stride_);
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@ -458,19 +463,16 @@ namespace
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CV_Assert( img.type() == CV_8UC1 || img.type() == CV_8UC4 );
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CV_Assert( win_stride_.width % block_stride_.width == 0 && win_stride_.height % block_stride_.height == 0 );
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CV_Assert( !stream );
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BufferPool pool(stream);
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GpuMat block_hists = pool.getBuffer(1, getTotalHistSize(img.size()), CV_32FC1);
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computeBlockHistograms(img, block_hists);
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BufferPool pool(stream);
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GpuMat block_hists = pool.getBuffer(1, getTotalHistSize(img.size()), CV_32FC1);
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Size wins_per_img = numPartsWithin(img.size(), win_size_, win_stride_);
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Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
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const size_t block_hist_size = getBlockHistogramSize();
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Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
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Size wins_per_img = numPartsWithin(img.size(), win_size_, win_stride_);
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_descriptors.create(wins_per_img.area(), static_cast<int>(blocks_per_win.area() * block_hist_size), CV_32FC1);
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GpuMat descriptors = _descriptors.getGpuMat();
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GpuMat descriptors = _descriptors.getGpuMat();
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computeBlockHistograms(img, block_hists, stream);
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switch (descr_format_)
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{
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@ -490,7 +492,8 @@ namespace
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img.rows, img.cols,
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block_hists.ptr<float>(),
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cell_size_.width, cells_per_block_.width,
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descriptors);
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descriptors,
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StreamAccessor::getStream(stream));
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break;
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default:
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CV_Error(cv::Error::StsBadArg, "Unknown descriptor format");
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@ -504,18 +507,25 @@ namespace
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return static_cast<int>(block_hist_size * blocks_per_img.area());
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}
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void HOG_Impl::computeBlockHistograms(const GpuMat& img, GpuMat& block_hists)
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void HOG_Impl::computeBlockHistograms(const GpuMat& img, GpuMat& block_hists, Stream& stream)
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{
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BufferPool pool(stream);
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cv::Size blocks_per_win = numPartsWithin(win_size_, block_size_, block_stride_);
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hog::set_up_constants(nbins_, block_stride_.width, block_stride_.height, blocks_per_win.width, blocks_per_win.height, cells_per_block_.width, cells_per_block_.height);
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float angleScale = static_cast<float>(nbins_ / CV_PI);
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GpuMat grad = pool.getBuffer(img.size(), CV_32FC2);
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GpuMat qangle = pool.getBuffer(img.size(), CV_8UC2);
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BufferPool pool(Stream::Null());
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hog::set_up_constants(nbins_, block_stride_.width, block_stride_.height, blocks_per_win.width, blocks_per_win.height, cells_per_block_.width, cells_per_block_.height, StreamAccessor::getStream(stream));
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GpuMat grad = pool.getBuffer(img.size(), CV_32FC2);
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GpuMat qangle = pool.getBuffer(img.size(), CV_8UC2);
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computeGradient(img, grad, qangle);
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block_hists.create(1, getTotalHistSize(img.size()), CV_32FC1);
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switch (img.type())
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{
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case CV_8UC1:
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hog::compute_gradients_8UC1(nbins_, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction_);
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break;
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case CV_8UC4:
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hog::compute_gradients_8UC4(nbins_, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction_, StreamAccessor::getStream(stream));
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break;
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}
|
||||
|
||||
hog::compute_hists(nbins_,
|
||||
block_stride_.width, block_stride_.height,
|
||||
@ -524,7 +534,8 @@ namespace
|
||||
(float)getWinSigma(),
|
||||
block_hists.ptr<float>(),
|
||||
cell_size_.width, cell_size_.height,
|
||||
cells_per_block_.width, cells_per_block_.height);
|
||||
cells_per_block_.width, cells_per_block_.height,
|
||||
StreamAccessor::getStream(stream));
|
||||
|
||||
hog::normalize_hists(nbins_,
|
||||
block_stride_.width, block_stride_.height,
|
||||
@ -532,24 +543,8 @@ namespace
|
||||
block_hists.ptr<float>(),
|
||||
(float)threshold_L2hys_,
|
||||
cell_size_.width, cell_size_.height,
|
||||
cells_per_block_.width, cells_per_block_.height);
|
||||
}
|
||||
|
||||
void HOG_Impl::computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle)
|
||||
{
|
||||
grad.create(img.size(), CV_32FC2);
|
||||
qangle.create(img.size(), CV_8UC2);
|
||||
|
||||
float angleScale = (float)(nbins_ / CV_PI);
|
||||
switch (img.type())
|
||||
{
|
||||
case CV_8UC1:
|
||||
hog::compute_gradients_8UC1(nbins_, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction_);
|
||||
break;
|
||||
case CV_8UC4:
|
||||
hog::compute_gradients_8UC4(nbins_, img.rows, img.cols, img, angleScale, grad, qangle, gamma_correction_);
|
||||
break;
|
||||
}
|
||||
cells_per_block_.width, cells_per_block_.height,
|
||||
StreamAccessor::getStream(stream));
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user